Identifying the Characteristics that Lead to Effective Data Analytics Deployment in Human Resource Management—A Paradigm for Consequences

Vasudha Kurikala; V.Parvathi

doi:10.47297/wspchrmWSP2040-800503.20231401

PDF
Export
Share
Collection
Album

Identifying the Characteristics that Lead to Effective Data Analytics Deployment in Human Resource Management—A Paradigm for Consequences

Volume 14, Issue 1

Views: 0 Downloads: 275

Identifying the Characteristics that Lead to Effective Data Analytics Deployment in Human Resource Management—A Paradigm for Consequences
Identifying the Characteristics that Lead to Effective Data Analytics Deployment in Human Resource Management—A Paradigm for Consequences
GITAM School of Business, GITAM (Deemed to be University), Hyderabad, India
Vol. 14, Issue 1, Pages: 60-89(2023)
Published： 01 March 2023 ，
DOI：10.47297/wspchrmWSP2040-800503.20231401
Accepted：

Scan QR Code

Kurikala Vasudha,V.Parvathi Vasudha.Identifying the Characteristics that Lead to Effective Data Analytics Deployment in Human Resource Management—A Paradigm for Consequences[J].Journal of Chinese Human Resources Management,2023,14(01):60-89.
DOI：

Kurikala Vasudha,V.Parvathi Vasudha.Identifying the Characteristics that Lead to Effective Data Analytics Deployment in Human Resource Management—A Paradigm for Consequences[J].Journal of Chinese Human Resources Management,2023,14(01):60-89. DOI： 10.47297/wspchrmWSP2040-800503.20231401.

摘要

Abstract

The capacity of data analytics to give intuitions grounded on data-driven decision-making methods has contributed to human resource management seeing a rise in the significance of the field. However

as it is difficult to successfully incorporate an analytics-based approach into HRM

many firms cannot use HR Analytics. In the first step of this process

we will use a "framework synthesis" technique to identify the challenges that will prevent HRA from being implemented. The next step is developing a conceptual framework describing the various organizational elements that impact HRA usage. Using this paradigm

organizations can determine whether or not they should employ HRA. This study analyzes the significant aspects associated with the many technical

executive

ecological

and distinct elements that drive HRA adoption. One of these factors is the governance of the data. In addition

the research presented in this article identifies 23 sub-dimensions of these five criteria as the essential components that must be present for HRA to be successfully implemented and practiced within businesses. These sub-dimensions are the vital components that must be present for HRA to be successfully implemented and practiced within enterprises. In addition

we investigate the framework's implications for HR executives

HR administrators

CEOs

IT executives

and accessing experts regarding the operative implementation of HRA in enterprises.

关键词

Keywords

Conceptual frameworkEnterprisesPolicy making

references

Abawajy, J. (2012). User preference of cyber security awareness delivery methods. Behaviour & Information Technology, 33(3): 237-248.

Agarwal, R., & Dhar, V. (2014). Editorial—big data, data science, and analytics: The opportunity and challenge for is research. Information Systems Research, 25(3): 443-448.

Ajzen, I. (2012). Values, attitudes, and behavior. Methods, Theories, and Empirical Applications in the Social Sciences, 33-38.

Fishbein, M. (2000). Attitudes and the attitude-behavior relation: Reasoned and automatic processes. European Review of Social Psychology, 11(1): 1-33.

Al, N. E., Al, N. H., Mohamed, N. & Al-Jaroodi, J. (2015). Applications of big data to smart cities. Journal of Internet Services and Applications, 6: 25.

Andersen, M. K. (2017). Human capital analytics: The winding road. Journal of Organizational Effectiveness: People and Performance, 4(2): 133-136.

Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: Why HR is set to fail the big data challenge. Human Resource Management Journal, 26(1): 1-11.

Aral, S., Brynjolfsson, E., & Wu, L. (2012). Three-way complementarities: Performance pay, human resource analytics, and information technology. Management Science, 58(5): 913-931.

Aydiner, A., Tatoglu, E., Bayraktar, E., Zaim, S., & Delen, D. (2019). Business analytics and firm performance: The mediating role of business process performance. Journal of Business Research, 96: 228-237.

Bandura, A. (1972). Self-efficacy: The exercise of control. W.H. Freeman. Barrett, M., Davidson, E., Prabhu, J., & Vargo, S. L. (2015). Service innovation in the digital age: Key contributions and future directions. MIS Quarterly, 39(1): 135-154.

Barton, D., & Court, D. (2012). Making advanced analytics work for you. Harvard Business Review, 90(78/83): 128.

Tanya, B., Emma, P., & Elfi F. (2017). Electronic HRM: Four decades of research on adoption and consequences. The International Journal of Human Resource Management, 28(1): 98-131.

Boudreau, J., & Cascio, W. (2017). Human capital analytics: Why are we not there? Journal of Organizational Effectiveness: People and Performance, 4(2): 119-126.

Boudreau, J., & Lawler, E. (2015). Making talent analytics and reporting into a decision science. Working paper, centre for effective organizations. The University of Southern California.

Boudreau, J., & Ramstad, P. (2005). Talents, talent segmentation, and sustainability: A new HR decision science paradigm for a new strategy definition. Human Resource Management, 44(2): 129-136.

Boudreau, J. W., & Ramstad, P. M. (2007). Beyond HR: The new science of human capital. Boston: Harvard Business School Press.

Brunton, G., Oliver, S., & Thomas, J. (2020). Innovations in framework synthesis as a systematic review method. Res Syn Meth., 11: 316-330.

Brynjolfsson, E., Hitt, L. M., & Kim, H. H. (2011). Strength in numbers: How does data-driven decision-making affect firm performance? SSRN Electronic Journal. https://doi. org/10.2139/ssrn.1819486.

Camps, J., & Luna-Arocas, R. (2010). A matter of learning: How human resources affect organizational performance. British Journal of Management. https://doi.org/ 10.1111/j.1467-8551.2010.00714.x.

Carroll, C., Booth, A., & Cooper, K. (2011). A worked example of "best fit" framework synthesis is a systematic review of views concerning taking some potential chemopreventive agents. BioMed Central Medical Research Methodology, 11, 29.

Chau, P. Y., & Hui, K. L. (2001). Determinants of small business EDI adoption: An empirical investigation. Journal of Organizational Computing and Electronic Commerce, 11(4): 229-252.

Chen, J., Chen, Y., Du, X., Li, C., Lu, J., Zhao, S., & Zhou, X. (2013). Big data challenge: A data management perspective. Frontiers of Computer Science, 7(2): 157-164.

Cheng, M. M., & Hackett, R. D. (2019). A critical review of algorithms in HRM: Definition, theory, and practice. Human Resource Management Review, 100698. https://doi.org/10.1016/j.hrmr.2019.100698.

Chwelos, P., Benbasat, I., & Dexter, A. S. (2001). Empirical test of an EDI adoption model. Information Systems Research, 12(3): 304-321.

Corte-Real, N., Ruivo, P., Oliveira, T., & Popoviˇc, A. (2019). Unlocking the drivers of considerable data analytics value in firms. Journal of Business Research, 97: 160-173.

Cronin, B., Morath, R., Curtin, P., & Heil, M. (2006). Public sector use of technology in managing human resources. Human Resource Management Review, 16(3): 416-430.

Dahlbom, P., Siikanen, N., Sajasalo, P., & Jarvenpää, M. (2019). Big data and HR analytics in the digital Era. Baltic Journal of Management, 15(1): 120-138.

Davenport, T. (2006). Competing on analytics. Harvard Business Review. https://hbr.Org/2006/01/competing-on-analytics.

Davenport. (2012). Data scientist: The sexiest job of the 21st century. Harvard Business Review. https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century.

Davenport, T. H. (2018). From analytics to artificial intelligence. Journal of Business Analytics, 1(2): 73-80.

Daylami, N., Ryan, T., Olfman, L., & Shayo, C. (2005). Proceedings of the 38th annual Hawaii international conference, Big Island, HI, January 3-6.

Deloitte. (2015) HR and people analytics. Stuck in Neutral. https://www2.deloitte.com/ us/en/insights/focus/human-capital-trends/2015/people-and-hr-analytics-human capital-trends-2015.html.

DePietro, R., Wiarda, E., & Fleischer, M. (1990). The context for change: Organization, technology, and environment. In L. G. Tornatzky, & M. Fleischer (Eds.), The processes of technological innovation, 151-175.

Lexington, MA: Lexington Books. DiClaudio, M. (2019). People analytics and the rise of HR: How data, analytics, and emerging technology can transform human resources (HR) into a profit center. Strategic HR Review, 18(2): 42-46.

Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges, and research agenda. International Journal of Information Management, 48: 63-71.

Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and plans for research, practice, and policy. International Journal of Information Management, 57.

Dwivedi, Y. K., Khan, N., & Papazafeiropoulou, A. (2007). Consumer adoption and usage of broadband in Bangladesh. Electronic Government, an International Journal, 4(3): 299-313.

Dwivedi, Y. K., Rana, N. P., Janssen, M., Lal, B., Williams, M. D., & Clement, M. (2017). Empirical validation of a unified model of electronic government adoption (UMEGA). Government Information Quarterly, 34(2): 211-230.

Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3): 719-734.

Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2): 897-904.

Esteves, J., & Curto, J. (2013). A risk and benefits behavioral model to assess intentions to adopt big data. Journal of Intelligence Studies in Business, 3(1): 37-46.

Fernandez, J. (2019). The ball of wax we call HR analytics. Strategic HR Review, 18(1): 21-25.

Fitz-End, J., Phillips, P., Ray, R. (2012). Human capital analytics: A primer. The conference board. Predictive Analytics for Human Resources.

John Wiley & Sons Inc. Gale, N., Heath, G., Cameron, E., Rashid, S., & Redwood, S. (2013). Using the framework method for analyzing qualitative data in multi-disciplinary health research. BMC Medical Research Methodology, 2013(13): 117.

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2): 137-144.

Gelbard, R., Ramon-Gonen, R., Carmeli, A., Bittmann, R. M., & Talyansky, R. (2018). Sentiment analysis in organizational work: Towards an ontology of people analytics. Expert Systems, 35(5).

Goode, S & Stevens, K. (2000). An analysis of the business characteristics of adopters and non-adopters of world wide web technology. Information Technology and Management, 1: 129-154.

Greasley, K., & Thomas, P. (2020). HR analytics: The onto-epistemology and politics of metricized HRM. Human Resource Management Journal.

Green, D. (2017). The best practices to Excel at people analytics. Journal of Organizational Effectiveness: People and Performance, 4(2): 137-144.

Grover, V. (1993). An empirically derived model for the adoption of customer-based inter-organizational systems. Decision Sciences, 24(3): 603-640.

Haeger, D. L., & Lingham, T. (2014). A trend toward work-life fusion: A multi-generational shift in technology use at work. Technological Forecasting and Social Change, 89: 316-325.

Hamilton, R., & Sodeman, W. A. (2020). We ask for opportunities and challenges for using big data analytics to manage human capital resources strategically. Business Horizons, 63(1): 85-95.

Hashem, I. A., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Ullah Khan, S. (2015). The rise of "big data" on cloud computing: Review and open research issues. Information Systems, 47: 98-115.

Houghton, C., Murphy, K., Meehan, B., Thomas, J., Brooker, D., & Casey, D. (2017). From screening to synthesis: Using Nvivo enhances transparency in qualitative evidence synthesis. Journal of Clinical Nursing, 26(5/6): 873-881.

Hsu, C., & Lin, J. C. (2016). Factors affecting the adoption of cloud services in enterprises. Inf Syst E-Bus Manage, 14: 791-822.

Huselid, M. A. (2018). The science and practice of workforce analytics: Introduction to the HRM special issue. Human Resource Management, 57(3): 679-684.

Jeyaraj, A., Rottman, J. W., & Lacity, M. C. (2006). A review of the predictors, linkages, and biases in IT innovation adoption research. Journal of Information Technology, 21(1): 1-23.

Kapoor, K. K., Dwivedi, Y. K., & Williams, M. D. (2014a). Rogers' innovation adoption attributes a systematic review and synthesis of existing research. Information Systems Management, 31(1): 74-91.

Kapoor, K. K., Dwivedi, Y. K., & Williams, M. D. (2014b). Innovation adoption attributes a review and synthesis of research findings. European Journal of Innovation Management, 17(3): 327-348.

Karacapilidis, N., Tzagarakis, M., & Christodoulou, S. (2013). On meaningful exploitation of machine and human reasoning to tackle data-intensive decision-making. Intelligent Decision Technologies, 7(3): 225-236.

Kossek, E. E., Young, W., Gash, D. C., & Nichol, V. (1994). Waiting for innovation in the human resources department: Godot implements a human resource information system. Human Resource Management, 33(1): 135-159.

Kostkova, P., Brewer, H., De Lusignan, S., Fottrell, E., Goldacre, B., Hart, G., Koczan, P., Knight, P., Marsolier, C., McKendry, R. A., Ross, E., Sasse, A., Sullivan, R., Chaytor, S., Stevenson, O., Velho, R., & Tooke, J. (2016). Who owns the data? Open data for healthcare. Frontiers in Public Health, 4: 7.

Abrinidis, A., & Jagadish, H. V. (2012). Challenges and opportunities with big data. Proceedings of the VLDB Endowment, 5(12): 2032-2033.

LaValle, L., Shockley, H., & Kruschwitz, (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 2011.

Lawler, E. E., Levenson, A., & Boudreau, J. W. (2004). Hr metrics and analytics: Use and impact. Human Resource Planning, 27: 27-35.

Lee, J. (2004). Discriminant analysis of technology adoption behavior: A case of internet technologies in small businesses. Journal of Computer Information Systems, 44(4): 57-66.

Levenburg, N. M., & Klein, H. A. (2006). Delivering customer services online: Identifying best practices of medium-sized enterprises. Information Systems Journal, 16(2): 135-155.

Levenson, A. (2005). Harnessing the power of HR analytics. Strategic HR Review, 4(3): 28-31.

Levenson, A. (2011). Using targeted analytics to improve talent decisions. People and Strategy, 34(2): 34.

Levenson, A. (2017). Using workforce analytics to improve strategy execution. Human Resource Management, 57(3): 685-700.

Lin, C. H., Sanders, K., Sun, J.-M. J., Shipton, H., & Mooi, E. A. (2016). From customeroriented strategy to organizational financial performance: The role of human resource management and customer-linking capability. British Journal of Management, 27(1): 21-37.

Lipkin, J. (2015). Sieving through the data to find the person: HR's imperative for balancing big data with people centricity. Cornell HR Review, 1-5.

Chen, Y., & Wu, M. (2011). Understanding the determinants of cloud computing adoption. Industrial Management & Data Systems, 111(7): 1006-1023.

Maduku, D. K., Mpinganjira, M., & Duh, H. (2016). Understanding mobile marketing adoption intention by South African SMEs: A multi-perspective framework. International Journal of Information Management, 36(5): 711-723.

M¨akinen, S. J., Kanniainen, J., & Dedehayir, O. (2013). Adoption dynamics of increasing return technologies in systemic contexts. Technological Forecasting and Social Change, 80(6): 1140-1146.

Marler, J. H., & Boudreau, J. W. (2016). An evidence-based review of HR analytics. The International Journal of Human Resource Management, 28(1): 3-26.

Martinsons, M. G. (1994). Benchmarking human resource information systems in Canada and Hong Kong. Information & Management, 26(6): 305-316.

McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data. The management revolution. Harvard Business Review, 90(10): 61-67.

Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98: 261-276.

Minbaeva, D. B. (2017). Building credible human capital analytics for organizational competitive advantage. Human Resource Management, 57(3): 701-713.

Musawa, M. S., & Wahab, E. (2012). Adopting electronic data interchange (EDI) technology by Nigerian SMEs: A conceptual framework. Journal of Business Management and Economics, 3(2): 55-68.

Ngai, E. W. T., Karen, K. L. M., Riggins, F. J., & Candace, Y. Y. (2008). RFID research: An academic literature review (1995–2005) and future research directions. International Journal of Production Economics, 112(2): 510-520.

Ozgen, K. (2013). Self-efficacy beliefs in mathematical literacy and connections between mathematics and the natural world: high school students' case. Journal of International Education Research, 9(4): 305-316.

Panayotopoulou, L., Vakola, M., & Galanaki, E. (2007). E-HR adoption and the role of HRM: Evidence from Greece. Personnel Review, 36: 277-294.

Pape, T. (2016). Prioritizing data items for business analytics: Framework and application to human resources. European Journal of Operational Research, 252(2): 687-698.

Premkumar, G., & Roberts, M. (1999). Adoption of new information technologies in rural small businesses. Omega, 27(4): 467-484.

Provost, F & Fawcett, T. (2013). Data science for business. Ramaswamy, S., (2013). What the companies winning at big data do differently. Bloomberg, June: http://www. Bloomberg. Com/news/2013-06-25/what-the companies-winning-at-big-data-do-differently.html.

Rasmussen, T., & Ulrich, D. (2015). Learning from practice: How HR analytics avoids being a management fad. Organizational Dynamics, 44(3): 236-242.

Ritchie, J. & Spencer, L. (1993). Qualitative data analysis for applied policy research. In A. Bryman, R. Burgess (eds.), Analysing Qualitative Data.

London: Routledge. Rogers, E.M. (2003). Diffusion of innovations. New York: Free Press.

Schiemann, W. A., Seibert, J. H., & Blankenship, M. H. (2017). Putting human capital analytics to work: Predicting and driving business success. Human Resource Management, 57(3): 795-807.

Scullen, S. E., Mount, M. K., & Goff, M. (2000). Understanding the latent structure of job performance ratings. Journal of Applied Psychology, 85(6): 956-970.

Shet, S. V. (2020c). Leadership: Making an impact, inspiring organization, and getting to the next level. Human Resource Development International, 24(2): 1-6.

Shet, Patil, Chandawarkar (2019a). Competency-based superior performance and organizational effectiveness. International Journal of Productivity and Performance Management, 68(4): 753-773.

Shet, Sateesh. V. (2020b). Strategic talent management–contemporary issues in an international context. Human Resource Development International, 23(1): 98-102.

Simon, C., & Ferreiro, E. (2017). Workforce analytics: A case study of scholar-practitioner collaboration. Human Resource Management, 57(3): 781-793.

Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of significant data challenges and analytical methods. Journal of Business Research, 70: 263-286.

Stone, D. L., Deadrick, D. L., Lukaszewski, K. M., & Johnson, R. (2015). The influence of technology on the future of human resource management. Human Resource Management Review, 25(2): 216-231.

Stone, D. L., & Dulebohn, J. H. (2013). Emerging issues in theory and research on electronic, human resource management (e-HRM). Human Resource Management Review, 23(1): 1-5.

Strohmeier, S., & Piazza, F. (2013). Domain-driven data mining in human resource management: A review of current research. Expert Systems with Applications, 40(7): 2410-2420.

Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4): 15-42.

Tansley, C., & Newell, S. (2007). A knowledge-based view of agenda-formation in the development of human resource information systems. Management Learning, 38(1): 95-119.

Thomas, J., O'Mara-Eves, A., Harden, A., & Newman, M. (2017). Synthesis methods for combining and configuring textual or mixed methods data. In D. Gough, S. Oliver, & J. Thomas (Eds.). An Introduction to Systematic Reviews, 2: 211-250.

Thong, J. (1999). An integrated model of information systems adoption in small businesses. Journal of Management Information Systems, 15(4): 187-214.

Thong, J. Y. L., Yap, C. S., & Raman, K. S. (1996). Top management support, external expertise, and information systems implementation in small businesses. Information Systems Research, 7(2): 248-267.

Tong, A., Flemming, K., McInnes, E., Oliver, S., & Craig, J. (2012). Enhancing transparency in reporting the synthesis of qualitative research: ENTREQ. BMC Medical Research Methodology, 12(1).

Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington: Lexington Books.

Torraco, R. J. (2005). Writing integrative literature reviews: Guidelines and examples. Human Resource Development Review, 4(3): 356-367.

Tursunbayeva, A., Di Lauro, S., & Pagliari, C. (2018). People analytics: A scoping review of conceptual boundaries and value propositions. International Journal of Information Management, 43: 224-247.

Vargas, R., Yurova, Y. V., Ruppel, C. P., Tworoger, L. C., & Greenwood, R. (2018). Individual adoption of HR analytics: A fine-grained view of the early stages leading to adoption. The International Journal of Human Resource Management, 29(22): 3046-3067.

Venkatesh, V., Thong, J., & Xu, X. (2016). A unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5): 328-376.

Wamba, S., Gunasekaran, A., Akter, S., Ren, S., Dubey, R., & Childe, S. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70: 356-365.

Wang, Y., & Hajli, N. (2017). Exploring the path to big data analytics success in healthcare. Journal of Business Research, 70: 287-299.

Waters, S. D., Streets, V., Johnson-Murray, R., & McFarlane, L. (2018). The practical guide to HR analytics: Using data to inform, transform, and empower HR decisions. SHRM.

Wieringa, J., Kannan, P., Ma, X., Reutterer, T., Risselada, H. and Skiera, B. (2019). Data analytics in a privacy-concerned world. Journal of Business Research, 122: 915-925.

Yasmin, M., Tatoglu, E., Kilic, H., Zaim, S., & Delen, D. (2020). Extensive data analytics capabilities and firm performance: An integrated MCDM approach. Journal of Business Research, 114: 1-15.

Yeung, J. H., Shim, J. P., & Lai, A. Y. (2003). The current progress of e-commerce adoption. Communications of the ACM, 46(9): 226-232.

Yi, X., Liu, F., Liu, J., & Jin, H. (2014). Building a network highway for big data: Architecture and challenges. IEEE Network, 28(4): 5-13.

Alert me when the article has been cited

Submit

No data

Related Author

No data

Related Institution

No data

⁰